The Machine Learning team within Parallel Graph AnalytiX (PGX) group at Oracle Labs has open
internship positions available on Graph Machine Learning development topics.
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Oracle Labs is the advanced research and development arm of Oracle. We focus on the development of technologies that keep Oracle at the forefront of the computer industry. Oracle Labs researchers look for novel approaches and methodologies, often taking on projects with high risk or uncertainty, or that are difficult to tackle within a product- development organization. Oracle Labs research is focused on real-world outcomes: our researchers aim to develop technologies that will someday play a significant role in the evolution of technology and society. For example, chip multithreading and the Java programming language grew out of work done in Oracle Labs.
Parallel Graph AnalytiX (PGX)
Relationships in the data are becoming a key feature to enable knowledge discovery from large datasets. Graphs are a powerful abstraction to support this analysis, thanks to their explicit representation of relationships as edges. Graph analysis lets you reveal latent information that is encoded, not as fields in your data, but as direct and indirect relationships between elements of your data – information that is not obvious to the naked eye, but can have tremendous value once uncovered.
PGX is a toolkit for graph analysis that supports running
(i) algorithms such as PageRank on graphs, (ii) performing SQL-like pattern-matching on graphs using the results of algorithmic analysis, and (iii) graph machine learning techniques like DeepWalk or Graph Neural Networks. Algorithms are parallelized for extreme performance. The PGX toolkit includes both a single-node in-memory engine, and a distributed engine for extremely large graphs. Graphs can be loaded from a variety of sources including flat files, SQL and NoSQL databases and Apache Spark and Hadoop; incremental updates are supported.
PGX is both already available as an option in Oracle products and an active research project at Oracle Labs, with a world-class team of researchers further advancing the capabilities of the toolkit.
At Oracle Labs PGX group, we are actively working on challenging machine learning problems with a focus on graph-represented data. We are developing a state-of-the-art Machine Learning library primarily to support various Graph- based ML techniques. These specific graph-based algorithms mainly deal with vector representations of (i) vertices in a graph, or (ii) sub-graphs, or (iii) even a complete graph. The use-cases of these approaches are vast and spans across multiple domains starting from Finance, Bio-med, to Cybersecurity. While implementing these functionalities, our focus is on the following objectives.
The goal of this project is to design and implement novel graph learning
algorithms (or optimize existing algorithms) that scale-up on large-scale graphs
by accounting for the above-mentioned challenges.
The successful candidate is expected to complete the internship using a wide and diverse set of skills.
For more information, contact Rhicheek Patra.